Generative AI offers great potential to revolutionize both business operations and daily life. However, this possibility depends heavily on trust. Loss of trust in AI can have far-reaching effects, including discouraging investment, hindering adoption, and reducing reliance on these systems.
Just as the industry has traditionally prioritized server, network, and application security, now AI is emerging as the next major platform, requiring robust security measures. Given the impending integration into business frameworks, it is important to build in security measures from the beginning. Integrating security into AI models and applications early in the development process ensures that trust remains intact and facilitates a smooth transition from proof of concept to production.
Driving this change means looking to new data to understand how today's executives are trying to secure generative AI and how to navigate and prioritize these AI security initiatives. It means developing a plan of action that will help you rank.
An executive perspective on generative AI
Most AI projects are driven by business and operations teams, so security leaders have a deep understanding of business priorities and participate in these conversations from a risk-driven perspective.
Our latest research takes a deep dive into global executives' perspectives and priorities regarding the risks and adoption of generative AI. The findings reveal a worrying gap between security concerns and the desire for rapid innovation. A significant 82% of respondents recognize the importance of secure and reliable AI for business success, but a surprising 69% still prioritize innovation over security.
In the UK, CEOs are similarly focused on productivity as a key driver, but are increasingly looking to operational, technology and data leaders as strategic decision makers. This is also reflected in his 2023 CEO Survey, which highlighted the increasing influence of technology and his leaders on decision-making. His 38% of CEOs cite the CIO as the most important decision maker in their organization, followed by the chief technology officer (26%).
Drive change by navigating and prioritizing AI security
To successfully navigate these challenges, companies need a framework to protect their generative AI. It starts with the recognition that AI does pose a security risk as long as models are trained on centralized and sensitive data. Therefore, that data must be protected from the threat of theft and tampering.
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Security regarding the development of new models must also be tight. As new AI applications are devised and their training methods evolve, enterprises must be mindful of the potential for new vulnerabilities to be introduced into the broader system architecture. Therefore, in addition to increasing integration and strictly enforcing policies regarding access to sensitive systems, companies must always be on the lookout for deficiencies. An attacker also tries to hijack or manipulate the behavior of his AI model using model inference. Therefore, enterprises need to protect the use of AI models by expediting data detection or exfiltration, warning about evasion, poisoning, exfiltration, or inference attacks.
It's also important to remember that one of your first lines of defense is ensuring a secure infrastructure. Businesses of all types need to improve network security, access control, data encryption, and intrusion detection and prevention for their AI security environments. Organizations should also consider investing in new security defenses specifically designed to protect AI from hacking and hostile operations.
With new regulations and public scrutiny of responsible AI on the horizon, robust AI governance will play a bigger role in putting operational guardrails in place to effectively manage a company’s AI security strategy. will play a role. After all, a model that operationally deviates from its designed purpose can pose the same level of risk as an adversary compromising a business's infrastructure.
protect the present for the future
Above all, the transformative potential of generative AI relies on trust, making robust security measures essential. Compromises in AI security could hinder investment and adoption and undermine reliance on these systems. Just as securing servers and networks becomes a priority, AI is emerging as the next major platform that requires rigorous security. Integrating security measures early in AI development is critical to maintaining trust and facilitating a smooth transition to production.
Understanding executives’ perspectives and priorities regarding AI security is essential, especially given the gap between security concerns and the desire for rapid innovation. To address these challenges, frameworks for securing generative AI must focus on data protection, model development, and usage. Additionally, securing your infrastructure and implementing robust AI governance is essential to mitigating risk and ensuring AI works as intended.
